Imaging Biomarkers for Precision Medicine in Locally Advanced Breast Cancer

Guidelines from the American National Comprehensive Cancer Network (NCCN)recommend neoadjuvant chemotherapy (NAC) to patients with locally advanced breast cancer (LABC) to downstage tumors before surgery. However, only a small fraction (15-17%) of LABC patients achieve complete pathologic response (pCR), i.e. no residual tumor in the breast, after treatment. Measuring tumor response during 53 neoadjuvant chemotherapy can potentially help physicians adapt treatment thus, potentially improving the pCR rate. Recently, imaging biomarkers that are used to measure the tumor’s functional and biological features have been studied as pre-treatment markers for pCR or as an indicator for intra-treatment tumor response. Also, imaging biomarkers have been the focus of intense research to characterize tumor heterogeneity as well as to advance our understanding of the principle mechanisms behind chemoresistance. Advances in investigational radiology are moving rapidly to high-resolution imaging, capturing metabolic data, performing tissue characterization and statistical modelling of imaging biomarkers, with an endpoint of personalized medicine in breast cancer treatment. In this commentary, we present studies within the framework of imaging biomarkers used to measure breast tumor response to chemotherapy. Current studies are showing that significant progress has been made in the accuracy of measuring tumor response either before or during chemotherapy, yet the challenges at the forefront of these works include translational gaps such as needing large-scale clinical trials for validation, and standardization of imaging methods. However, the ongoing research is showing that imaging biomarkers may play an important role in personalized treatments for LABC

Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer

Aim: We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer. Materials & methods: Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers. Results: There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%. Conclusion: Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori

Measuring Chemotherapy Response in Breast Cancer Using Optical and Ultrasound Spectroscopy

Purpose: This study comprises two subprojects. In subproject one, the study purpose was to evaluate response to neoadjuvant chemotherapy (NAC) using quantitative ultrasound (QUS) and diffuse optical spectroscopy imaging (DOS) in locally advanced breast cancer (LABC) during chemotherapy. In subproject two, DOS-based functional maps were analysed with texture-based image features to predict breast cancer response before the start of NAC. Patients and Measurements: The institution’s ethics review board approved this study. For subproject one, subjects (n=22) gave written consent before participating in the study. Participants underwent non-invasive, DOS and QUS imaging. Data were acquired at weeks 0 (i.e. baseline), 1, 4, 8 and before surgical removal of the tumour (mastectomy and/or lumpectomy); corresponding to chemotherapy schedules. QUS parameters including the midband fit (MBF), 0-MHz intercept (SI), and the spectral slope (SS) were determined from tumour ultrasound data using spectral analysis. In the same patients, DOS was used to measure parameters relating to tumour haemoglobin and tissue composition such as %Water and %Lipids. Discriminant analysis and receiver-operating characteristic (ROC) analyses were used to correlate the measured imaging parameters to Miller-Payne pathological response during treatment. Additionally, multivariate analysis was carried out for pairwise DOS and QUS parameter combinations to determine if an increase in the classification accuracy could be obtained using combination DOS and QUS parametric models. For subproject two, 15 additional patients we recruited after first giving their written informed consent. A pooled analysis was completed for all DOS baseline data (subproject 1 and subproject 2; n=37 patients). LABC patients planned for NAC had functional DOS maps and associated textural features generated. A grey-level co-occurrence matrix (texture) analysis was completed for parameters associated with haemoglobin, tissue composition, and optical properties (deoxy-haemoglobin [Hb], oxy-haemoglobin [HbO2], total haemoglobin [HbT]), %Lipids, %Water, and scattering power [SP], scattering amplitude [SA]) prior to treatment. Textural features included contrast (con), vi correlation (cor), energy (ene), and homogeneity (hom). Patients were classified as ‘responders’ or ‘non-responders’ using Miller-Payne pathological response criteria after treatment completion. In order to test if baseline univariate texture features could predict treatment response, a receiver operating characteristic (ROC) analysis was performed, and the optimal sensitivity, specificity and area under the curve (AUC) was calculated using Youden’s index (Q-point) from the ROC. Multivariate analysis was conducted to test 40 DOS-texture features and all possible bivariate combinations using a naïve Bayes model, and k-nearest neighbour (k-NN) model classifiers were included in the analysis. Using these machine-learning algorithms, the pretreatment DOS-texture parameters underwent dataset training, testing, and validation and ROC analysis were performed to find the maximum sensitivity and specificity of bivariate DOS-texture features. Results: For subproject one, individual DOS and QUS parameters, including the spectral intercept (SI), oxy-haemoglobin (HbO2), and total haemoglobin (HbT) were significant markers for response outcome after one week of treatment (p<0.01). Multivariate (pairwise) combinations increased the sensitivity, specificity and AUC at this time; the SI+HbO2 showed a sensitivity/specificity of 100%, and an AUC of 1.0 after one week of treatment. For subproject two, the results indicated that textural characteristics of pre-treatment DOS parametric maps can differentiate treatment response outcomes. The HbO2-homogeneity resulted in the highest accuracy amongst univariate parameters in predicting response to chemotherapy: sensitivity (%Sn) and specificity (%Sp) = 86.5 and 89.0%, respectively and an accuracy of 87.8%. The highest predictors using multivariate (binary) combination features were the Hb-Contrast + HbO2-Homogeneity which resulted in a %Sn = 78.0, a %Sp = 81.0% and an accuracy of 79.5% using the naïve Bayes model. Conclusion: DOS and QUS demonstrated potential as coincident markers for treatment response and may potentially facilitate response-guided therapies. Also, the results of this study demonstrated that DOS-texture analysis can be used to predict breast cancer response groups prior to starting NAC using baseline DOS measurements

Novel formulations for magnetic-resonance imaging guided theranostics

Recent advances in bioimaging, biochemistry and bioinformatics have facilitated the development of personalized and precision medicine. Theranostics, a combination of imaging modalities and therapeutic agents, have garnered increasing attention in this context, thanks to their potential to monitor and control treatment for individual patients. An attractive strategy to achieve this goal involves the development of therapy guided by magnetic resonance imaging (MRI). MRI, possessing a number of benefits including a high degree of soft tissue contrast, low invasiveness, high depth of penetration and good spatial resolution, could offer advanced imaging-guided therapy enabling precise and time-resolved assessment of disease conditions and therapeutic progression. The goal of this PhD thesis is to develop novel formulations based on polymeric, inorganic or hybrid materials using two pharmaceutical fabrication techniques (electrohydrodynamic atomisation or spray drying), and explore their potential in MRI-guided chemotherapy. Five different types of formulation carrying MRI contrast agents and chemotherapeutic agents were fabricated. Chapter 3 reports the fabrication of pH-responsive formulations via electrodynamic atomization, loaded with superparamagnetic iron oxide nanoparticles (SPIONs) as contrast agents and the model chemotherapeutic carmofur. These platforms are able to protect the cargo from release acidic conditions representative of the stomach, while at neutral pH the relaxivity is tightly correlated to the extent of drug release. Chapter 4 describes a series of dual responsive systems with distinct morphology, comprising of pH-responsive Eudragit shells with SPIONs, and thermo-responsive core loading carmofur. The fibres are found to have better thermo-responsive properties compared to microparticles, and the relaxivity display clear linear relationships with drug release data. Chapter 5 focuses on using spray drying to fabricate nano-in-micro particles based on a synthetic polymer with an upper capital solution temperature. The microparticles encapsulate drug-loaded layered double hydroxide nanosheets, have thermo-sensitive release and relaxivity profile, and in vitro cell studies reveal that the formulations permit synergistic hyperthermia-aided chemotherapy. Chapter 6 details the preparation and characterization of four gadolinium doped layered double hydroxides to develop theranostic platforms carrying chemotherapeutics with high T1-relaxivity. In Chapter 7, polydopamine-coated polycaprolactone/poly(lactic-co-glycolic) acid nanofibers are developed via co-axial electrospinning, which are loaded with dug-loaded LDH nanocomposites in the core. In vitro studies reflect sustained release of chemotherapeutics, and highly effective cytotoxic effects on tumour cells with the polydopamine coated formulations, which was further enhanced at higher levels of glutathione

Nanomedicine Formulations Based on PLGA Nanoparticles for Diagnosis, Monitoring and Treatment of Disease: From Bench to Bedside

Nanomedicine is among the most promising emerging fields that can provide innovative and radical solutions to unmet needs in pharmaceutical formulation development. Encapsulation of active pharmaceutical ingredients within nano-size carriers offers several benefits, namely, protection of the therapeutic agents from degradation, their increased solubility and bioavailability, improved pharmacokinetics, reduced toxicity, enhanced therapeutic efficacy, decreased drug immunogenicity, targeted delivery, and simultaneous imaging and treatment options with a single system.Poly(lactide-co-glycolide) (PLGA) is one of the most commonly used polymers in nanomedicine formulations due to its excellent biocompatibility, tunable degradation characteristics, and high versatility. Furthermore, PLGA is approved by the European Medicines Agency (EMA) and the Food and Drug Administration (FDA) for use in pharmaceutical products. Nanomedicines based on PLGA nanoparticles can offer tremendous opportunities in the diagnosis, monitoring, and treatment of various diseases.This Special Issue aims to focus on the bench-to-bedside development of PLGA nanoparticles including (but not limited to) design, development, physicochemical characterization, scale-up production, efficacy and safety assessment, and biodistribution studies of these nanomedicine formulations

High Yield Production and Biofunctionalization of Plasma Polymerized Nanoparticles for Applications in Biomedicine

The advent of nanoparticle technology in the medical and pharmaceutical sectors offers significant potential in the quest for more effective healthcare solutions. Surface biofunctionalization, the engineering of nanoparticle surfaces for targeted biomedical applications, is critical for the development of personalized medicine in the delivery of diagnostic and therapeutic agents. However, the transition from laboratory research to commercial scale production faces substantial challenges in ensuring scalability of synthesis methods, consistent nanoparticle quality and addressing safety concerns related to toxicity. The commercial viability of nanoparticle-based medical products requires the precedence of translation into clinical settings warranting their integration into cellular environments while maintaining bioactivity. Traditional methods for nanoparticle synthesis and functionalization often fall short from achieving this aim due to inconsistencies in particle sizes, high production costs, reagent toxicity and subsequent complex wet-chemical processing. This thesis introduces plasma polymerization as a high-throughput method for producing surface- active polymeric nanoparticles, through a more environmentally friendly, dry synthesis process. Chapter 1 introduces polymer nanoparticles in nanomedicine, focusing on surface functionalization for improved diagnostics and therapeutics, and overcoming biological barriers for targeted delivery. Chapter 2 outlines methodologies. Chapter 3 discusses enhanced collection yield while maintaining properties for surface treatment, demonstrated through biofunctionalization of plasma polymerized nanoparticles (PPNs) with non-cytotoxic covalent bonds. Chapter 4 confirms this via in vitro studies. Chapter 5 studies long-term stability, showing bioactivity after a year. The final chapter details PPN synthesis, validating a continuum fluid model to enhance predictive accuracy

Extracellular vesicles from induced neurons trigger epigenetic silencing of a brain neurotransmitter.

Introduction: Antithrombin (AT) is a glycoprotein involved in the regulation of blood coagulation. It belongs to the family of serine-protease inhibitors and acts as the most important antagonist of different clot- ting factors. Two types of inherited AT deficiency can be distinguished: Type I (quantitative deficit), and Type II (qualitative deficit). The latter is characterized by an impaired inhibitory activity related to dysfunc- tional domains of the protein. Three Type II subtypes can be defined: Type IIa (reactive site defect), Type IIb (heparin binding site defect) and Type IIc (pleiotropic defect). This classification has clinical importance since these subtypes have a different thrombotic risk. No functional routine diagnostic assay, however, can be assumed to detect all forms of Type II deficiencies since false-negative results may hamper the diagnosis. Methods: We analysed the biochemical/biophysical association of ATT to EVs. We separated EVs from plasma of healthy or Type II affected patients or from cultured hepatocytes through differential ultracentrifu- gation followed by sucrose density gradient and/or immunoprecipitation. We next combined dot blot ana- lysis, WB, 2D electrophoresis and enzymatic assays to reveal the nature of ATT association to EVs. Results: We evidenced that ATT is associated to the external leaflet of EVs. We also found that specific ATT isoforms are enriched in EV preparations in respect to total plasma and that those isoforms are selectively associated to EVs when comparing healthy or ATT type II deficient patients. Summary/Conclusion: ATT selective association pat- tern to EVs might be related either to mutations in the primary sequence of the protein or alterations in the glycosylation process, hence experiments are ongoing to reveal the nature of this phenomenon. Our findings suggest that analysis of ATT enriched in EV prepara- tions might be useful to gain insights into the patho- genesis and be of support in the diagnostic algorithm of ATT deficiency. Funding: This work acknowledges FFABR (Fondo finanziamento attività Base di ricerca from MIUR, Ministry of Education, Universities and Research, Italy) for financial support